the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Seasonal Snow-Atmosphere Modeling: Let's do it
Abstract. Mountain snowpack forecasting relies on accurate mass and energy input information to the snowpack. For this reason, coupled snow-atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extent of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow-atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal time scales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial LiDAR data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model output suggesting that parameterizations of the process using surface wind fields only may be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model output during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain, and a low-bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and studying the representation of snow accumulation and ablation processes.
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Status: open (until 25 May 2024)
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CC1: 'Comment on egusphere-2024-489', Yang Yu, 23 Apr 2024
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The paper “Seasonal Snow-Atmosphere Modeling: Let’s do it” developed a model coupled a snowpack model with an atmosphere model. The model is verified through comparing the simulating results with the Li-DAR data. The results about snow preferential deposition, snow redistribution, snow melt and the near surface temperature is discussed. The overall model results show great agrements with the experiments results at hectometer scale in mountainous area. The writing and organization are good. Here are some suggestions:
- In the introduction part, the snow preferential deposition background knowledge is introduction in details, but the introduction about how radiation influence the snow melt is a little less.
- Section 2.1 about the model coupling, a framework figure would help a lot.
- Section 2.2, paragraph 2 (line 114 and line 117), how a reference in 2002 (Doorschot and Lehning, 2007) explains the shortness of the scheme in 2007 (Liston et al., 2007).
- Section 2.2, line 125, “standard halo exchange’ better quote a paper for reader to understand.
- Section 3.1.1, as the snowfall process is highly related to the wind field, a wind speed rose map of the studying area could help explain the results in figure 2.
- The font size in figure 6 is smaller than other figure, unify the font size for all figures helps.
- What is the dark solid line means in the last subfigure in figure 7?
- Line 277 – 301, these paragraphs discuss the influence of the 3D flow field to the cloud microphysics and the near-surface particle-flow interactions, paper “Huang N, Yu Y, Shao Y, et al. Numerical Simulation of Falling‐Snow Deposition Pattern Over 3D‐Hill[J]. Journal of Geophysical Research: Atmospheres, 2024, 129(2): e2023JD039898.” could support these discussions.
- At the first paragraph of section 3.2, author write “A low bias in high-elevation albedo, combined with a slight warm bias, could explain this excessive melt.” to explain the low snow melt at higher elevation in the model results. May be this is because the atmosphere model lacks the physics process called “temperature inversion”. Due to strong radiation cooling based on lower humidity and cold air run-off from a higher mountain region into the valley or basin by a local circulation, the air temperature in the valley or basin becomes lower and lower during night in wintertime, and temperature is higher at high elevation. This process occurred and a temperature inversion layer exited almost every day during wintertime. This physics process could also explain the error in the near surface air temperature modeling. Author could refer to paper “Du, M., et al. "Temperature distribution in the high mountain regions on the Tibetan Plateau-Measurement and simulation." Proc. MODSIM 2007 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand. 2007.”.
- If the “temperature inversion” do matters, the conclusion about the snowmelt and air temperature parts need rewrite.
Citation: https://doi.org/10.5194/egusphere-2024-489-CC1
Model code and software
HICARsnow Model Code Dylan Reynolds https://doi.org/10.5281/zenodo.10679464
Video supplement
Preferential Deposition Processes Dylan Reynolds https://doi.org/10.16904/envidat.482
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